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Awesome H2O

A curated list of research, applications and projects built using the H2O Machine Learning platform

Here you can see meta information about this topic like the time we last updated this page, the original creator of the awesome list and a link to the original GitHub repository.

Last Update: Dec. 4, 2021, 11:19 a.m.

Thank you h2oai & contributors
View Topic on GitHub:
h2oai/awesome-h2o

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Blog Posts & Tutorials

Books

Research Papers

Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.

0
0
1y 72d
n/a

Repository of my thesis "Understanding Random Forests"

489
151
5y 5m
n/a

Benchmarks

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

1.83K
338
2y 108d
MIT

Presentations

Courses

Software

Splashing a User Interface onto h2o MOJO Files

4
0
9m
n/a

Model Wrappers for H2O models

17
2
8m
n/a

A repository for deploying an AWS EMR cluster and submiting spark jobs on it. Boostrapping by default does inclues pysparkling so one can easily use h2o with python and spark.

6
4
4y 5m
Apache-2.0